Improved image edge detection method

Abstract

The invention discloses an improved image edge detection method. The improved image edge detection method comprise steps of S1, performing smoothing processing on an image and using an improved media filter to suppress noise, S2, obtaining a difference between a horizontal direction and a perpendicular direction through first-order-partial derivatives of directions of x,y,45 degrees and 135 degrees and thus obtaining a gradient amplitude and a gradient direction, S3, performing non-maximum value inhibition on the gradient amplitude, S4, using a gradient histogram to solve a high threshold and a low threshold and then using a double-threshold algorithm to perform edge detection on the image, and S5, performing sharpening processing and connecting the edges to obtain a final edge image. The improved image edge detection method use weighted media filtering to replace gauss filtering, uses the partial derivatives of four directions, uses the gradient histogram to determine the high threshold and the low threshold, reduces detection errors, improves detection accuracy, and makes the counter of the edge image more clear and continuity better.

Description

technical field [0001] The invention specifically relates to an improved image edge detection method. Background technique [0002] The image edge refers to the collection of pixels around which the grayscale of pixels is discontinuous or extremely varied, and is also the dividing line between the target, the background and the region. Edge detection first detects the edge points in the image, and then connects the edge points into edge lines according to a certain strategy, and finally forms a segmentation area. Edge detection is the basis of feature extraction, object recognition, and image understanding. Therefore, it is the basic problem of image processing and computer vision. Image edge detection is mainly realized by four techniques: derivation operator, mathematical morphology, wavelet transform and image fusion. Among them, the derivation operator method is the most extensive edge detection technology, including Roberts operator, Sobel operator, Prewitt operator O...

AI Technical Summary

Problems solved by technology

Image edge detection is mainly realized by four techniques including derivative operator, mathematical morphology, wavelet transform and image fusion, among which the derivative operator method is the most extensive edge detection technology, including Roberts operator, Sobel operator, Prewitt operator, and so on. Operator, Laplace operator and Log operator, etc. These operators are simple and easy to implement, and have good real-time performance, but they are sensitive to noise interference, have poor anti-interference performance, and the edges are easily polluted by noise, so the detection effect is not ideal.